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<div class="title">direct_convolution5x5.cl</div>  </div>
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<a href="direct__convolution5x5_8cl.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2016, 2017 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="helpers_8h.xhtml">helpers.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">#undef CONVERT_SAT</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="preprocessor">#if defined(DATA_TYPE) &amp;&amp; defined(STRIDE_X) &amp;&amp; defined(WEIGHTS_DEPTH)</span></div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#if STRIDE_X == 1</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="preprocessor">#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr)</span></div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="preprocessor">#elif STRIDE_X == 2 </span><span class="comment">/* STRIDE_X == 1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="preprocessor">#define CONVOLUTION1x5(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr)</span></div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="preprocessor">#else </span><span class="comment">/* STRIDE_X not equals 1 or 2 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="preprocessor">#error &quot;STRIDE_X larger than 2 is not supported&quot;</span></div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* STRIDE_X == 2 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#define CONVOLUTION1x5_STRIDE1(acc, src_row_ptr, weights_row_ptr)                                                               \</span></div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">    ({                                                                                                                          \</span></div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                             \</span></div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">        weights_values0          = vload4(0, weights_row_ptr);                                                                  \</span></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">        DATA_TYPE weights_value1 = *(weights_row_ptr + 4);                                                                      \</span></div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                             \</span></div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">        src0 = vload8(0, src_row_ptr);                                                                                          \</span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                             \</span></div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">        src1 = vload4(0, src_row_ptr + 8);                                                                                      \</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">        \</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">        acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0;                                                          \</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \</span></div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \</span></div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1;     \</span></div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="preprocessor">    })</span></div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="preprocessor">#define CONVOLUTION1x5_STRIDE2(acc, src_row_ptr, weights_row_ptr)                                                               \</span></div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="preprocessor">    ({                                                                                                                          \</span></div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;<span class="preprocessor">        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                             \</span></div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="preprocessor">        weights_values0          = vload4(0, weights_row_ptr);                                                                  \</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="preprocessor">        DATA_TYPE weights_value1 = *(weights_row_ptr + 4);                                                                      \</span></div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="preprocessor">        VEC_DATA_TYPE(DATA_TYPE, 16)                                                                                            \</span></div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="preprocessor">        src0 = vload16(0, src_row_ptr);                                                                                         \</span></div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="preprocessor">        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                             \</span></div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;<span class="preprocessor">        src1 = vload4(0, src_row_ptr + 16);                                                                                     \</span></div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="preprocessor">        acc += src0.even * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0;                                                     \</span></div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1;         \</span></div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \</span></div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;<span class="preprocessor">        \</span></div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \</span></div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;<span class="preprocessor">        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1;     \</span></div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;<span class="preprocessor">    })</span></div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;__kernel <span class="keywordtype">void</span> direct_convolution5x5(</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    <a class="code" href="helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>),</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    <a class="code" href="helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <a class="code" href="helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(weights),</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;#ifdef HAS_BIAS</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    <a class="code" href="helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases),</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;#endif <span class="comment">/* defined(HAS_BIAS) */</span></div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_stride_w)</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;{</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    <a class="code" href="struct_image.xhtml">Image</a>    <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">src</a>     = <a class="code" href="helpers_8h.xhtml#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(src);</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> weights = <a class="code" href="helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a>(weights);</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>     = <a class="code" href="helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a>(dst);</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <a class="code" href="fixed__point_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    pixels0 = 0;</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    __global uchar *weights_addr = (__global uchar *)<a class="code" href="helpers_8h.xhtml#a2101b2fe0193ce227ae4e0945e321d85">tensor3D_offset</a>(&amp;weights, 0, 0, 0);</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    __global uchar *src_addr     = (__global uchar *)<a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&amp;src, 0, 0);</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    const <span class="keywordtype">int</span> kernel_index = get_global_id(2);</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    weights_addr += kernel_index * weights_stride_w;</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    for(volatile <span class="keywordtype">int</span> d = 0; d &lt; WEIGHTS_DEPTH; ++d)</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    {</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        CONVOLUTION1x5(pixels0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)src_addr, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)weights_addr);</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        CONVOLUTION1x5(pixels0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + 1 * src_stride_y), (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        CONVOLUTION1x5(pixels0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + 2 * src_stride_y), (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;        CONVOLUTION1x5(pixels0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + 3 * src_stride_y), (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(weights_addr + 3 * weights_stride_y));</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;        CONVOLUTION1x5(pixels0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + 4 * src_stride_y), (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(weights_addr + 4 * weights_stride_y));</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;        src_addr += src_stride_z;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;        weights_addr += weights_stride_z;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    }</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;<span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    pixels0 += (<a class="code" href="fixed__point_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)) * ((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&amp;biases, kernel_index)));</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    vstore8(pixels0, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)dst.<a class="code" href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a>);</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;}</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;<span class="preprocessor">#endif // defined(DATA_TYPE) &amp;&amp; defined(STRIDE_X) &amp;&amp; defined(WEIGHTS_DEPTH)</span></div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;<span class="preprocessor">#if defined(WEIGHTS_DEPTH)</span></div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;<span class="preprocessor">#define CONVOLUTION1x5_BIFROST(acc, src0, weights_row00, weights_row01) \</span></div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;<span class="preprocessor">    ({                                                                  \</span></div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;<span class="preprocessor">        acc.s0 = mad(src0.s0, weights_row00.s0, acc.s0);                \</span></div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;<span class="preprocessor">        acc.s1 = mad(src0.s1, weights_row00.s0, acc.s1);                \</span></div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;<span class="preprocessor">        acc.s2 = mad(src0.s2, weights_row00.s0, acc.s2);                \</span></div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;<span class="preprocessor">        acc.s3 = mad(src0.s3, weights_row00.s0, acc.s3);                \</span></div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;<span class="preprocessor">        acc.s0 = mad(src0.s1, weights_row00.s1, acc.s0);                \</span></div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;<span class="preprocessor">        acc.s1 = mad(src0.s2, weights_row00.s1, acc.s1);                \</span></div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;<span class="preprocessor">        acc.s2 = mad(src0.s3, weights_row00.s1, acc.s2);                \</span></div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;<span class="preprocessor">        acc.s3 = mad(src0.s4, weights_row00.s1, acc.s3);                \</span></div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;<span class="preprocessor">        acc.s0 = mad(src0.s2, weights_row00.s2, acc.s0);                \</span></div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;<span class="preprocessor">        acc.s1 = mad(src0.s3, weights_row00.s2, acc.s1);                \</span></div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;<span class="preprocessor">        acc.s2 = mad(src0.s4, weights_row00.s2, acc.s2);                \</span></div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;<span class="preprocessor">        acc.s3 = mad(src0.s5, weights_row00.s2, acc.s3);                \</span></div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;<span class="preprocessor">        acc.s0 = mad(src0.s3, weights_row00.s3, acc.s0);                \</span></div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;<span class="preprocessor">        acc.s1 = mad(src0.s4, weights_row00.s3, acc.s1);                \</span></div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;<span class="preprocessor">        acc.s2 = mad(src0.s5, weights_row00.s3, acc.s2);                \</span></div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;<span class="preprocessor">        acc.s3 = mad(src0.s6, weights_row00.s3, acc.s3);                \</span></div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;<span class="preprocessor">        acc.s0 = mad(src0.s4, weights_row01, acc.s0);                   \</span></div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;<span class="preprocessor">        acc.s1 = mad(src0.s5, weights_row01, acc.s1);                   \</span></div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;<span class="preprocessor">        acc.s2 = mad(src0.s6, weights_row01, acc.s2);                   \</span></div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;<span class="preprocessor">        acc.s3 = mad(src0.s7, weights_row01, acc.s3);                   \</span></div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;<span class="preprocessor">    })</span></div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;__kernel <span class="keywordtype">void</span> direct_convolution5x5_f32_bifrost(</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;    <a class="code" href="helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(src),</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    <a class="code" href="helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(dst),</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    <a class="code" href="helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(weights),</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;#ifdef HAS_BIAS</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    <a class="code" href="helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases),</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;#endif <span class="comment">/* defined(HAS_BIAS) */</span></div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weights_stride_w)</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;{</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    <span class="comment">// Get the kernel index</span></div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> kernel_index = get_global_id(2);</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <a class="code" href="struct_image.xhtml">Image</a>    src = <a class="code" href="helpers_8h.xhtml#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(src);</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> dst = <a class="code" href="helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a>(dst);</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    float4 pixels0 = 0.0f;</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    float4 pixels1 = 0.0f;</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    __global uchar *src_addr     = (__global uchar *)<a class="code" href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(&amp;src, 0, 0);</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    <span class="comment">// Note: Since each work-item computes 4x2 elements, we need to load 6 rows from the input tensor</span></div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    <span class="keywordflow">for</span>(ushort d = 0; d &lt; (ushort)WEIGHTS_DEPTH; ++d)</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    {</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        <span class="comment">// Load the weights from row0 and row1</span></div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        float4 weights_row00 = vload4(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;        <span class="keywordtype">float</span>  weights_row01 = *((__global <span class="keywordtype">float</span> *)(weights_addr + 0 * weights_stride_y) + 4);</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        float4 weights_row10 = vload4(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        <span class="keywordtype">float</span>  weights_row11 = *((__global <span class="keywordtype">float</span> *)(weights_addr + 1 * weights_stride_y) + 4);</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        float8 src0;</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        <span class="comment">// Load values from row0 of input tensor</span></div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        src0 = vload8(0, (__global <span class="keywordtype">float</span> *)(src_addr + 0 * src_stride_y));</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        <span class="comment">// Accumulate</span></div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        <span class="comment">// Load values from row1 of input tensor</span></div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        src0 = vload8(0, (__global <span class="keywordtype">float</span> *)(src_addr + 1 * src_stride_y));</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        <span class="comment">// Accumulate</span></div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11);</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        <span class="comment">// Load values from row2 of input tensor</span></div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;        src0 = vload8(0, (__global <span class="keywordtype">float</span> *)(src_addr + 2 * src_stride_y));</div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        <span class="comment">// Load weights from row2</span></div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;        weights_row00 = vload4(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        weights_row01 = *((__global <span class="keywordtype">float</span> *)(weights_addr + 2 * weights_stride_y) + 4);</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        <span class="comment">// Accumulate</span></div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);</div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11);</div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;</div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;        <span class="comment">// Load values from row3 of input tensor</span></div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        src0 = vload8(0, (__global <span class="keywordtype">float</span> *)(src_addr + 3 * src_stride_y));</div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;</div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;        <span class="comment">// Load weights from row3</span></div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;        weights_row10 = vload4(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 3 * weights_stride_y));</div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        weights_row11 = *((__global <span class="keywordtype">float</span> *)(weights_addr + 3 * weights_stride_y) + 4);</div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;        <span class="comment">// Accumulate</span></div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11);</div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;</div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;        <span class="comment">// Load values from row4 of input tensor</span></div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;        src0 = vload8(0, (__global <span class="keywordtype">float</span> *)(src_addr + 4 * src_stride_y));</div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;        <span class="comment">// Load weights from row4</span></div><div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;        weights_row00 = vload4(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 4 * weights_stride_y));</div><div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;        weights_row01 = *((__global <span class="keywordtype">float</span> *)(weights_addr + 4 * weights_stride_y) + 4);</div><div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;</div><div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);</div><div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11);</div><div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;</div><div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;        <span class="comment">// Load values from row5 of input tensor</span></div><div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        src0 = vload8(0, (__global <span class="keywordtype">float</span> *)(src_addr + 5 * src_stride_y));</div><div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;</div><div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;        <span class="comment">// Accumulate</span></div><div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);</div><div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;</div><div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;        src_addr += src_stride_z;</div><div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;        weights_addr += weights_stride_z;</div><div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    }</div><div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;</div><div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;<span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;    <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;</div><div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;    float4 bias = (float4) * ((__global <span class="keywordtype">float</span> *)(<a class="code" href="helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&amp;biases, kernel_index)));</div><div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;</div><div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;    pixels0 += bias;</div><div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;    pixels1 += bias;</div><div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;</div><div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    vstore4(pixels0, 0, (__global <span class="keywordtype">float</span> *)(dst.<a class="code" href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a> + 0 * dst_stride_y));</div><div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    vstore4(pixels1, 0, (__global <span class="keywordtype">float</span> *)(dst.<a class="code" href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a> + 1 * dst_stride_y));</div><div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;}</div><div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;<span class="preprocessor">#endif // defined(WEIGHTS_DEPTH)</span></div><div class="ttc" id="struct_vector_xhtml"><div class="ttname"><a href="struct_vector.xhtml">Vector</a></div><div class="ttdoc">Structure to hold Vector information. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00132">helpers.h:132</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a31c8c760f08fb1a331b16b7c204321dc"><div class="ttname"><a href="helpers_8h.xhtml#a31c8c760f08fb1a331b16b7c204321dc">CONVERT_TO_TENSOR3D_STRUCT</a></div><div class="ttdeci">#define CONVERT_TO_TENSOR3D_STRUCT(name)</div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00117">helpers.h:117</a></div></div>
<div class="ttc" id="convolution3x3_8cl_xhtml_afb8c72ce35c4a1f4a2588d6573e54aa1"><div class="ttname"><a href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="ttdeci">#define DATA_TYPE</div><div class="ttdef"><b>Definition:</b> <a href="convolution3x3_8cl_source.xhtml#l00027">convolution3x3.cl:27</a></div></div>
<div class="ttc" id="struct_tensor3_d_xhtml"><div class="ttname"><a href="struct_tensor3_d.xhtml">Tensor3D</a></div><div class="ttdoc">Structure to hold 3D tensor information. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00149">helpers.h:149</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a></div><div class="ttdeci">__global uchar * offset(const Image *img, int x, int y)</div><div class="ttdoc">Get the pointer position of a Image. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00301">helpers.h:301</a></div></div>
<div class="ttc" id="helpers_8h_xhtml"><div class="ttname"><a href="helpers_8h.xhtml">helpers.h</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_aebe814363556c244be043b13e7969197"><div class="ttname"><a href="helpers_8h.xhtml#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a></div><div class="ttdeci">#define CONVERT_TO_IMAGE_STRUCT(name)</div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00102">helpers.h:102</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a2101b2fe0193ce227ae4e0945e321d85"><div class="ttname"><a href="helpers_8h.xhtml#a2101b2fe0193ce227ae4e0945e321d85">tensor3D_offset</a></div><div class="ttdeci">__global const uchar * tensor3D_offset(const Tensor3D *tensor, int x, int y, int z)</div><div class="ttdoc">Get the pointer position of a Tensor3D. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00313">helpers.h:313</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a40a6eb9f2a7712f08d6bb8ff6c9e6ca7"><div class="ttname"><a href="helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a></div><div class="ttdeci">#define VECTOR_DECLARATION(name)</div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00060">helpers.h:60</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00137">Convolution.cpp:137</a></div></div>
<div class="ttc" id="struct_image_xhtml"><div class="ttname"><a href="struct_image.xhtml">Image</a></div><div class="ttdoc">Structure to hold Image information. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00140">helpers.h:140</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a6b83038822d1ae7ab619b684ed3b7fc0"><div class="ttname"><a href="helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a></div><div class="ttdeci">#define TENSOR3D_DECLARATION(name)</div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00074">helpers.h:74</a></div></div>
<div class="ttc" id="fixed__point_8h_xhtml_a36f754c05b6fddf6df0d8d0a74f8159f"><div class="ttname"><a href="fixed__point_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a></div><div class="ttdeci">#define VEC_DATA_TYPE(type, size)</div><div class="ttdef"><b>Definition:</b> <a href="fixed__point_8h_source.xhtml#l00093">fixed_point.h:93</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a79e8e562daa6599317d2d1cd86ef1bf2"><div class="ttname"><a href="helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a></div><div class="ttdeci">#define CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(name)</div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00121">helpers.h:121</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a64d779f80eeb923e0ab2313433f7b40b"><div class="ttname"><a href="helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a></div><div class="ttdeci">#define CONVERT_TO_VECTOR_STRUCT_NO_STEP(name)</div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00099">helpers.h:99</a></div></div>
<div class="ttc" id="helpers_8h_xhtml_a7e4940407322d6f0ccb8b6b86b856019"><div class="ttname"><a href="helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a></div><div class="ttdeci">__global const uchar * vector_offset(const Vector *vec, int x)</div><div class="ttdoc">Get the pointer position of a Vector. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00290">helpers.h:290</a></div></div>
<div class="ttc" id="struct_tensor3_d_xhtml_acf52c23cbd7424606c10a606524e3e32"><div class="ttname"><a href="struct_tensor3_d.xhtml#acf52c23cbd7424606c10a606524e3e32">Tensor3D::ptr</a></div><div class="ttdeci">__global uchar * ptr</div><div class="ttdoc">Pointer to the starting postion of the buffer. </div><div class="ttdef"><b>Definition:</b> <a href="helpers_8h_source.xhtml#l00151">helpers.h:151</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a6743f0a130e8311e6f5b1a23df102472"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a6743f0a130e8311e6f5b1a23df102472">arm_compute::test::validation::src</a></div><div class="ttdeci">convolution configure &amp; src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_convolution_8cpp_source.xhtml#l00147">Convolution.cpp:147</a></div></div>
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